
import pandas as pd
import numpy as np

import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px

from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_absolute_error, mean_squared_error


df = pd.read_csv(r"C:\Users\syed sahel\Downloads\Retail_sales.csv")
df.head()


# Show missing values
df.isnull().sum()

# Fill numeric missing values with median
num_cols = ["Units Sold", "Sales Revenue (USD)", "Discount Percentage", "Marketing Spend (USD)", "Holiday Effect"]
for col in num_cols:
    df[col] = df[col].fillna(df[col].median())

# Fill categorical missing values with mode
cat_cols = ["Store ID", "Product ID", "Store Location", "Product Category", "Day of the Week"]
for col in cat_cols:
    df[col] = df[col].fillna(df[col].mode()[0])

# Convert Date column
df["Date"] = pd.to_datetime(df["Date"])

# Extract date features
df["Year"] = df["Date"].dt.year
df["Month"] = df["Date"].dt.month
df["Day"] = df["Date"].dt.day
df["Week"] = df["Date"].dt.isocalendar().week


plt.figure(figsize=(12,5))
plt.plot(df["Date"], df["Sales Revenue (USD)"])
plt.title("Sales Revenue Over Time")
plt.xlabel("Date")
plt.ylabel("Revenue (USD)")
plt.show()


category_sales = df.groupby("Product Category")["Units Sold"].sum()

plt.figure(figsize=(8,5))
sns.barplot(x=category_sales.index, y=category_sales.values)
plt.title("Units Sold by Category")
plt.xticks(rotation=45)
plt.show()


location_revenue = df.groupby("Store Location")["Sales Revenue (USD)"].sum()

plt.figure(figsize=(15,5))
sns.barplot(x=location_revenue.index, y=location_revenue.values)
plt.title("Revenue by Store Location")
plt.xticks(rotation=45)
plt.show()


plt.figure(figsize=(7,5))
sns.scatterplot(x=df["Discount Percentage"], y=df["Sales Revenue (USD)"])
plt.title("Effect of Discount on Sales Revenue")
plt.show()


plt.figure(figsize=(8,6))
sns.heatmap(df.corr(), annot=True, cmap="coolwarm")
plt.title("Correlation Heatmap")
plt.show()

C:\Users\syed sahel\AppData\Local\Temp\ipykernel_1164\2585287783.py:2: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.
  sns.heatmap(df.corr(), annot=True, cmap="coolwarm")


# Revenue per unit
df["Revenue_per_Unit"] = df["Sales Revenue (USD)"] / df["Units Sold"]

# Discount impact score
df["Discount_Impact"] = df["Units Sold"] * df["Discount Percentage"]

# Holiday impact multiplied by unit sales
df["Holiday_Sales_Boost"] = df["Holiday Effect"] * df["Units Sold"]
df


feature_cols = [
    "Units Sold",
    "Discount Percentage",
    "Marketing Spend (USD)",
    "Holiday Effect",
    "Month",
    "Year"
]

X = df[feature_cols]
y = df["Sales Revenue (USD)"]


X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42
)


model = LinearRegression()
model.fit(X_train, y_train)

LinearRegression()


pred = model.predict(X_test)

print("MAE:", mean_absolute_error(y_test, pred))
print("MSE:", mean_squared_error(y_test, pred))
print("RMSE:", np.sqrt(mean_squared_error(y_test, pred)))

MAE: 1553.4563867802353
MSE: 4136687.2297183075
RMSE: 2033.8847631363749


future = pd.DataFrame({
    "Units Sold": [120],
    "Discount Percentage": [15],
    "Marketing Spend (USD)": [500],
    "Holiday Effect": [1],
    "Month": [12],
    "Year": [2025]
})

future_prediction = model.predict(future)
future_prediction

array([56176.93734149])


fig = px.line(df, x="Date", y="Sales Revenue (USD)", title="Interactive Sales Revenue Trend")
fig.show()

fig2 = px.bar(df, x="Product Category", y="Units Sold", color="Product Category", title="Units Sold by Category")
fig2.show()

fig3 = px.scatter(df, x="Marketing Spend (USD)", y="Sales Revenue (USD)", title="Marketing Spend vs Revenue")
fig3.show()

	Store ID	Product ID	Date	Units Sold	Sales Revenue (USD)	Discount Percentage	Marketing Spend (USD)	Store Location	Product Category	Day of the Week	Holiday Effect
0	Spearsland	52372247	2022-01-01	9	2741.69	20	81	Tanzania	Furniture	Saturday	False
1	Spearsland	52372247	2022-01-02	7	2665.53	0	0	Mauritania	Furniture	Sunday	False
2	Spearsland	52372247	2022-01-03	1	380.79	0	0	Saint Pierre and Miquelon	Furniture	Monday	False
3	Spearsland	52372247	2022-01-04	4	1523.16	0	0	Australia	Furniture	Tuesday	False
4	Spearsland	52372247	2022-01-05	2	761.58	0	0	Swaziland	Furniture	Wednesday	False

	Store ID	Product ID	Date	Units Sold	Sales Revenue (USD)	Discount Percentage	Marketing Spend (USD)	Store Location	Product Category	Day of the Week	Holiday Effect	Year	Month	Day	Week	Revenue_per_Unit	Discount_Impact	Holiday_Sales_Boost
0	Spearsland	52372247	2022-01-01	9	2741.69	20	81	Tanzania	Furniture	Saturday	False	2022	1	1	52	304.632222	180	0
1	Spearsland	52372247	2022-01-02	7	2665.53	0	0	Mauritania	Furniture	Sunday	False	2022	1	2	52	380.790000	0	0
2	Spearsland	52372247	2022-01-03	1	380.79	0	0	Saint Pierre and Miquelon	Furniture	Monday	False	2022	1	3	1	380.790000	0	0
3	Spearsland	52372247	2022-01-04	4	1523.16	0	0	Australia	Furniture	Tuesday	False	2022	1	4	1	380.790000	0	0
4	Spearsland	52372247	2022-01-05	2	761.58	0	0	Swaziland	Furniture	Wednesday	False	2022	1	5	1	380.790000	0	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
29995	Spearsland	50239115	2022-01-25	5	2501.15	0	100	Malawi	Clothing	Tuesday	False	2022	1	25	4	500.230000	0	0
29996	Spearsland	50239115	2022-01-26	3	1500.69	0	0	Sudan	Clothing	Wednesday	False	2022	1	26	4	500.230000	0	0
29997	Spearsland	50239115	2022-01-27	6	3001.38	0	0	South Georgia and the South Sandwich Islands	Clothing	Thursday	False	2022	1	27	4	500.230000	0	0
29998	Spearsland	50239115	2022-01-28	5	2501.15	0	0	Haiti	Clothing	Friday	False	2022	1	28	4	500.230000	0	0
29999	Spearsland	50239115	2022-01-29	3	1425.66	5	190	Mozambique	Clothing	Saturday	False	2022	1	29	4	475.220000	15	0

📊 Discount vs Revenue¶